| Title |
GAVIN: Gene-Aware Variant INterpretation for medical sequencing
|
|---|---|
| Published in |
Genome Biology, January 2017
|
| DOI | 10.1186/s13059-016-1141-7 |
| Pubmed ID | |
| Authors |
K. Joeri van der Velde, Eddy N. de Boer, Cleo C. van Diemen, Birgit Sikkema-Raddatz, Kristin M. Abbott, Alain Knopperts, Lude Franke, Rolf H. Sijmons, Tom J. de Koning, Cisca Wijmenga, Richard J. Sinke, Morris A. Swertz |
| Abstract |
We present Gene-Aware Variant INterpretation (GAVIN), a new method that accurately classifies variants for clinical diagnostic purposes. Classifications are based on gene-specific calibrations of allele frequencies from the ExAC database, likely variant impact using SnpEff, and estimated deleteriousness based on CADD scores for >3000 genes. In a benchmark on 18 clinical gene sets, we achieve a sensitivity of 91.4% and a specificity of 76.9%. This accuracy is unmatched by 12 other tools. We provide GAVIN as an online MOLGENIS service to annotate VCF files and as an open source executable for use in bioinformatic pipelines. It can be found at http://molgenis.org/gavin . |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 25% |
| United Kingdom | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 147 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | <1% |
| Unknown | 146 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 39 | 27% |
| Researcher | 21 | 14% |
| Student > Master | 17 | 12% |
| Student > Bachelor | 14 | 10% |
| Student > Postgraduate | 11 | 7% |
| Other | 17 | 12% |
| Unknown | 28 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 44 | 30% |
| Agricultural and Biological Sciences | 33 | 22% |
| Medicine and Dentistry | 21 | 14% |
| Computer Science | 8 | 5% |
| Neuroscience | 3 | 2% |
| Other | 8 | 5% |
| Unknown | 30 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 January 2022.
All research outputs
#6,496,106
of 25,374,647 outputs
Outputs from Genome Biology
#3,104
of 4,467 outputs
Outputs of similar age
#110,683
of 422,202 outputs
Outputs of similar age from Genome Biology
#43
of 61 outputs
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We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.